Authentication of Organically and Conventionally Grown Basils by Gas Chromatography/Mass Spectrometry Chemical Profiles

Abstract

Basil plants cultivated by organic and conventional farming practices were accurately classified by pattern recognition of gas chromatography/mass spectrometry (GC/MS) data. A novel extraction procedure was devised to extract characteristic compounds from ground basil powders. Two in-house fuzzy classifiers, i.e., the fuzzy rule-building expert system (FuRES) and the fuzzy optimal associative memory (FOAM) for the first time, were used to build classification models. Two crisp classifiers, i.e., soft independent modeling by class analogy (SIMCA) and the partial least-squares discriminant analysis (PLS-DA), were used as control methods. Prior to data processing, baseline correction and retention time alignment were performed. Classifiers were built with the two-way data sets, the total ion chromatogram representation of data sets, and the total mass spectrum representation of data sets, separately. Bootstrapped Latin partition (BLP) was used as an unbiased evaluation of the classifiers. By using two-way data sets, average classification rates with FuRES, FOAM, SIMCA, and PLS-DA were 100 ± 0%, 94.4 ± 0.4%, 93.3 ± 0.4%, and 100 ± 0%, respectively, for 100 independent evaluations. The established classifiers were used to classify a new validation set collected 2.5 months later with no parametric changes except that the training set and validation set were individually mean-centered. For the new two-way validation set, classification rates with FuRES, FOAM, SIMCA, and PLS-DA were 100%, 83%, 97%, and 100%, respectively. Thereby, the GC/MS analysis was demonstrated as a viable approach for organic basil authentication. It is the first time that a FOAM has been applied to classification. A novel baseline correction method was used also for the first time. The FuRES and the FOAM are demonstrated as powerful tools for modeling and classifying GC/MS data of complex samples and the data pretreatments are demonstrated to be useful to improve the performance of classifiers.

Development of Protein Biomarkers for the Authentication of Organic Rice

“…For example, the differentiation between organic rice and adulterated or conventional rice can be carried out by examining the differentially expressed specific marker protein profiles, and the relative ratios of the marker protein intensities visualized on 2-D gels. Other attempts to authenticate organic produce using metabolite analysis via gas chromatography-tandem MS (Wang et al, 2013b) or even an electronic nose (Peng et al, 2015) can also complement each other and together confirm the suitability of the selected diagnostic markers.…”

“…There have been many attempts to develop methods for identifying organic produce: developing "diagnostic" genes from high-and low-input agricultural regimes for the authentication of organic wheat via microarray analysis (Lu et al, 2005), identifying "diagnostic" proteins using 2D gels and the mass analysis of conventional and organic wheat (Zorb et al, 2009), and potato (Lehesranta et al, 2007) have shown that the authentication of organic produce could possibly be accomplished by establishing the specific protein signatures of produce grown under organic or conventional regimes. In addition, metabolomic fingerprinting has demonstrated that analytical tools can potentially be used for organic food authentication (Novotna et al, 2012;Gao et al, 2013;Wang et al, 2013b). However, the substantial fluctuations in the metabolome profiles of the crops due to differences in cultivation conditions can make metabolomic comparisons carried out in the interest of authenticating organic produce very complicated, and to some extent, meaningless.…”

Rapid detection of Fusarium wilt in basil (Ocimum sp.) leaves by desorption electrospray ionization mass spectrometry (DESI MS) imaging

“…Tandem mass spectral imaging was done on respective ionization modes to get the fragmentation pattern for identication of the predominant metabolite ion peaks. 26 Most of the studies to date on basil have tabulated information on both positive and negative ions of phenolics, avonoids and essential oils, etc. 3B and C. Elaborate details for nding metabolite adducts and matching tandem mass spectral data using database search is given in our previous report.…”

Spectral Representation of Proton NMR Spectroscopy for the Pattern Recognition of Complex Materials

“…The algorithm initiates by projecting the data from a multidimensional space onto a normalized weight vector to yield scalar scores [15] which are used to calculate the fuzzy entropy of classification. The fuzzy logistic values are the consequents of each rule, and the multivariate rules comprise the branches of the classification tree.…”