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.

Extracting Information and Enhancing the Quality of Separation Data: A Review on Chemometrics-Assisted Analysis of Volatile, Soluble and Colloidal Samples

“…This can be achieved with a GC hyphenated with an FID detector, thus in a fully untargeted way, which was exploited for different matrices. GC-FID was used to discriminate the geographical origins of extra-virgin olive oil [3] by PCA and PLS-DA, to understand the botanical origin of twelve classes of honey samples by LDA [26], the geographical origin of Prosecco wines, again by LDA [27], to compute a PLS model for the quantification of adulterations in saffron [28] and, recently, to evaluate the differences in tomato sauce due to brands and producers by PCA [29].…”

“…Works using IMS as a GC detector have increased over the last few years, also due to the cost reduction of the instruments [81]. It is also worth noting that often GC-IMS systems do not require the chemical pre-treatment of samples before the analysis, when working in head-space mode, making it a great advantage in terms of costs, but also of sample representability [29,82]. The second level of separation, however, produces two-dimension chromatograms, that require chemometrics for a proper elaboration [29].…”

“…It is also worth noting that often GC-IMS systems do not require the chemical pre-treatment of samples before the analysis, when working in head-space mode, making it a great advantage in terms of costs, but also of sample representability [29,82]. The second level of separation, however, produces two-dimension chromatograms, that require chemometrics for a proper elaboration [29]. Chen et al [83] used GC-IMS with PCA and CA to evaluate the change in rice VOCs during storage.…”

“…Within this framework the development of chemometric strategies exploiting only FFF-derived data can overall improve the efficiency of the study. Based on this concept Zappi et al reported an AF4-multidetection-PCA study on the colloidal content of tomato sauces analyzing only information provided by the AF4 platform such as selected portions of the UV fractograms of the samples or the trough colloidal ratio [29]. Interestingly, the results obtained with this minimalistic approach yielded better results than the one provided by an already established GC platform (working on tomato's volatile components) in terms of quality, while providing complementary information (Figure 9).…”

“…PCA score plots on AF4 data obtained for tomato sauce colloidal fraction, respectively, divided by: commercial brands (i.e., the retailer name on the label) and manufacturers. Adapted from [29].…”