Development of the mass spectral fingerprint by headspace-solid-phase microextraction-mass spectrometry and chemometric methods for rapid quality control of flavoring essence

Analysis of odor compounds in Lee Kum Kee brand oyster sauce and oyster enzymatic hydrolysate: Comparison and relationship

Oyster sauce (OS) is a highly processed oyster product. However, the significant price difference between OS and fresh oysters raises a question: Does authentic OS truly contain components from oysters or oyster enzymatic hydrolysates (OEH)? Therefore, the odor compounds of Lee Kum Kee oyster sauce (LKK), 4 OEHs, and 6 other seafood enzymatic hydrolysates (SEHs) were analyzed by using solid-phase microextraction and gas chromatography-olfactometry-mass spectrometry technology (SPME-GC-O-MS). The results of multivariate statistical analysis demonstrated the effective discrimination between LKK and OEHs from other SEHs. According to the VIP value and the differences in the composition of odor compounds among different samples, 15 essential odor compounds were screened out, which could distinguish whether the samples contained OEHs. Among them, acetic acid, 2-pentylfuran, 2-ethyl furan, 2-methylbutanal, and nonanal were only detected in LKK and OEHs, which further indicated the existence of OEH in LKK.

Method Comparison of Sample Pretreatment and Discovery of Differential Compositions of Natural Flavors and Fragrances for Quality Analysis by Using Chemometric Tools

Natural flavors and fragrances or their extracts have been widely used in a large variety of areas, including food, cosmetic, and tobacco industrial processes, among others. The compositions and intrinsic attributes of flavors and fragrances were related to many factors, such as species, geographical origin, planting environment, storage condition, processing method, and so on. This not only increased the difficulty in analyzing the product quality of flavors and fragrances, but also challenged the idea of "quality-by-design (QbD)". This work proposed an integrated strategy for precise discovery of differential compounds among different classes and subsequent quality analysis of complex samples through flavors and fragrances used in tobacco industry as examples. Three pretreatment methods were first inspected to effectively characterize the sample compositions, including direct injection (DI), thermal desorption (TD), and stir bar sorptive extraction (SBSE)-TD, coupled with gas chromatography-mass spectrometry (GC-MS) analysis to obtain characteristic information of samples of flavors and fragrances. Then, principal component analysis (PCA) was applied to discover the relation and difference between chromatographic fingerprints and peak table data once significant components were recognized in a holistic manner. Model population analysis (MPA) was then used to quantitatively extract the characteristic chemicals representing the quality differences among different classes of samples. Some differential marker compounds were discovered for difference analysis, including benzyl alcohol, latin acid, l-menthol acid, decanoic acid ethyl ester, vanillin, trans-o-coumaric acid, benzyl benzoate, and so on. Furthermore, partial least squares-discriminant analysis (PLS-DA) and support vector machine (SVM) were respectively applied to construct multivariate models for evaluation of quality differences and variations. It was found that the accuracy attains to 100% for sample classification. With the help of optimal sample pretreatment technique and chemometric methods, the strategy for quality analysis and difference discovery proposed in this work can be widely delivered to more areas of complex plants with good interpretability and high accuracy.

HS-SPME-MS-Enose Coupled with Chemometrics as an Analytical Decision Maker to Predict In-Cup Coffee Sensory Quality in Routine Controls: Possibilities and Limits

The quality assessment of the green coffee that you will go to buy cannot be disregarded from a sensory evaluation, although this practice is time consuming and requires a trained professional panel. This study aims to investigate both the potential and the limits of the direct headspace solid phase microextraction, mass spectrometry electronic nose technique (HS-SPME-MS or MS-EN) combined with chemometrics for use as an objective, diagnostic and high-throughput technique to be used as an analytical decision maker to predict the in-cup coffee sensory quality of incoming raw beans. The challenge of this study lies in the ability of the analytical approach to predict the sensory qualities of very different coffee types, as is usual in industry for the qualification and selection of incoming coffees. Coffees have been analysed using HS-SPME-MS and sensory analyses. The mass spectral fingerprints (MS-EN data) obtained were elaborated using: (i) unsupervised principal component analysis (PCA); (ii) supervised partial least square discriminant analysis (PLS-DA) to select the ions that are most related to the sensory notes investigated; and (iii) cross-validated partial least square regression (PLS), to predict the sensory attribute in new samples. The regression models were built with a training set of 150 coffee samples and an external test set of 34. The most reliable results were obtained with acid, bitter, spicy and aromatic intensity attributes. The mean error in the sensory-score predictions on the test set with the available data always fell within a limit of ±2. The results show that the combination of HS-SPME-MS fingerprints and chemometrics is an effective approach that can be used as a Total Analysis System (TAS) for the high-throughput definition of in-cup coffee sensory quality. Limitations in the method are found in the compromises that are accepted when applying a screening method, as opposed to human evaluation, in the sensory assessment of incoming raw material. The cost-benefit relationship of this and other screening instrumental approaches must be considered and weighed against the advantages of the potency of human response which could thus be better exploited in modulating blends for sensory experiences outside routine.

Rapidly monitoring the quality of flavoring essence based on microwave-induced plasma ionization mass spectrometry and multivariate statistical analysis

Microwave-induced plasma ionization mass spectrometry (MIPI-MS) combined with multivariate statistical analysis was first applied to rapidly monitor the quality of tobacco flavoring essence. A small isolation and reaction chamber was set up between the ion source and the injection port of mass spectrometer to effectively eliminate the interference of external environment in the process of analyzing samples. The improved experimental apparatus (MIPI-MS) could achieve online and high-throughput analysis, with minimal sample preparation steps. Further, two types of tobacco flavoring essences with the similar appearance and physicochemical parameters were employed to verify the usability of the promising method in the field of quality monitoring. Firstly, the mass spectral fingerprint of each essence was established by the improved MIPI-MS method within 2 min. Then, two multivariate statistical processes were carried out to analyze mass spectral data. The similarity results indicated that the thresholds of tobacco flavoring essences from different batches were 1.512 and 2.638, respectively. The first three principal components of the established PLS-DA described 93.6% of the total variability, and provided a visualized comparison for the two types of flavoring materials. Finally, the adulterated samples were successfully distinguished by employing the two multivariate statistical processes.

Comparison of flavour fingerprint, electronic nose and multivariate analysis for discrimination of extra virgin olive oils

Flavour is a special way to discriminate extra virgin olive oils (EVOOs) from other aroma plant oils. In this study, different ratios (5, 10, 15, 20, 30, 50, 70 and 100%) of peanut oil (PO), corn oil (CO) and sunflower seed oil (SO) were discriminated from raw EVOO using flavour fingerprint, electronic nose and multivariate analysis. Fifteen different samples of EVOO were selected to establish the flavour fingerprint based on eight common peaks in solid-phase microextraction-gas chromatography-mass spectrometry corresponding to 4-methyl-2-pentanol, (E)-2-hexenal, 1-tridecene, hexyl acetate, (Z)-3-hexenyl acetate, (E)-2-heptenal, nonanal and α-farnesene. Partial least square discrimination analysis (PLS-DA) was used to differentiate EVOOs and mixed oils containing more than 20% of PO, CO and SO. Furthermore, better discrimination efficiency was observed in PLS-DA than PCA (70% of CO and SO), which was equivalent to the correlation coefficient method of the fingerprint (20% of PO, CO and SO). The electronic nose was able to differentiate oil samples from samples containing 5% mixture. The discrimination method was selected based on the actual requirements of quality control.

Characterization of the volatile components in green tea by IRAE-HS-SPME/GC-MS combined with multivariate analysis

In the present work, a novel infrared-assisted extraction coupled to headspace solid-phase microextraction (IRAE-HS-SPME) followed by gas chromatography-mass spectrometry (GC-MS) was developed for rapid determination of the volatile components in green tea. The extraction parameters such as fiber type, sample amount, infrared power, extraction time, and infrared lamp distance were optimized by orthogonal experimental design. Under optimum conditions, a total of 82 volatile compounds in 21 green tea samples from different geographical origins were identified. Compared with classical water-bath heating, the proposed technique has remarkable advantages of considerably reducing the analytical time and high efficiency. In addition, an effective classification of green teas based on their volatile profiles was achieved by partial least square-discriminant analysis (PLS-DA) and hierarchical clustering analysis (HCA). Furthermore, the application of a dual criterion based on the variable importance in the projection (VIP) values of the PLS-DA models and on the category from one-way univariate analysis (ANOVA) allowed the identification of 12 potential volatile markers, which were considered to make the most important contribution to the discrimination of the samples. The results suggest that IRAE-HS-SPME/GC-MS technique combined with multivariate analysis offers a valuable tool to assess geographical traceability of different tea varieties.

Evaluation of cigarette flavoring quality via surface-enhanced Raman spectroscopy

Herein, surface-enhanced Raman spectroscopy (SERS) combined with principal component analysis (PCA) has been successfully applied in the evaluation of cigarette flavoring quality using monolayer films of Au nanoparticles as substrates.

A Novel and Practical Chromatographic "Fingerprint-ROC-SVM" Strategy Applied to Quality Analysis of Traditional Chinese Medicine Injections: Using KuDieZi Injection as a Case Study

Fingerprinting is widely and commonly used in the quality control of traditional Chinese medicine (TCM) injections. However, current studies informed that the fingerprint similarity evaluation was less sensitive and easily generated false positive results. For this reason, a novel and practical chromatographic “Fingerprint-ROC-SVM” strategy was established by using KuDieZi (KDZ) injection as a case study in the present article. Firstly, the chromatographic fingerprints of KDZ injection were obtained by UPLC and the common characteristic peaks were identified with UPLC/Q-TOF-MS under the same chromatographic conditions. Then, the receiver operating characteristic (ROC) curve was used to optimize common characteristic peaks by the AUCs value greater than 0.7. Finally, a support vector machine (SVM) model, with the accuracy of 97.06%, was established by the optimized characteristic peaks and applied to monitor the quality of KDZ injection. As a result, the established model could sensitively and accurately distinguish the qualified products (QPs) with the unqualified products (UPs), high-temperature processed samples (HTPs) and high-illumination processed samples (HIPs) of KDZ injection, and the prediction accuracy was 100.00%, 93.75% and 100.00%, respectively. Furthermore, through the comparison with other chemometrics methods, the superiority of the novel analytical strategy was more prominent. It indicated that the novel and practical chromatographic “Fingerprint-ROC-SVM” strategy could be further applied to facilitate the development of the quality analysis of TCM injections.

Use of Foodomics for Control of Food Processing and Assessing of Food Safety

Food chain, food safety, and food-processing sectors face new challenges due to globalization of food chain and changes in the modern consumer preferences. In addition, gradually increasing microbial resistance, changes in climate, and human errors in food handling remain a pending barrier for the efficient global food safety management. Consequently, a need for development, validation, and implementation of rapid, sensitive, and accurate methods for assessment of food safety often termed as foodomics methods is required. Even though, the growing role of these high-throughput foodomic methods based on genomic, transcriptomic, proteomic, and metabolomic techniques has yet to be completely acknowledged by the regulatory agencies and bodies. The sensitivity and accuracy of these methods are superior to previously used standard analytical procedures and new methods are suitable to address a number of novel requirements posed by the food production sector and global food market.