High-throughput foodomics strategy for screening flavor components in dairy products using multiple mass spectrometry

Effect of industrial wastewater treatment system upgrade on the composition of emitted odorants and volatile organic compounds from a cheese production facility

This study investigates the rarely studied volatile organic compound emissions from a cheese production facility and the impact of its wastewater treatment system upgrade on the composition of emitted odorants. Wastewater grab samples were collected from six separate wastewater channels before (2019) and after (2021) the system upgrade and analyzed for volatile organic compounds, pH, total dissolved solids, and electrical conductivity. Results showed that the channel from hard cheese production in 2021 had the highest number of volatile organic compounds (35), followed by the fresh cheese production channel (22). Following the industrial wastewater treatment system upgrade, a mineral oil contamination occurred; however, the number of odorants with nasal impact frequency (NIF) ≥ 0.5 in the effluent decreased from 11 to 5. 2-Propenoic acid butyl ester (NIF 0.75) stood out as the most prominent compound, described as fruity, waxy, or green. After the industrial wastewater treatment system upgrades, we observed a decrease in the number of odorants. However other measures must be taken to ensure proper wastewater processing. PRACTITIONER POINTS: More than 60 VOCs were identified in 6 channels from the cheese production facility.15 odorants in cheese production wastewater were detected by SPME-GC-MS/O. The most potent odorants before and after the system upgrade were 1-octen-3-ol and 2-propenoic acid butyl ester, respectively. The upgrades of the industrial wastewater treatment system had a positive impact on reducing the number of odorants and their odor intensity.

Shelf-Life Prediction and Critical Value of Quality Index of Sichuan Sauerkraut Based on Kinetic Model and Principal Component Analysis

Kinetic models and accelerated shelf-life testing were employed to estimate the shelf-life of Sichuan sauerkraut. The texture, color, total acid, microbe, near-infrared analysis, volatile components, taste, and sensory evaluation of Sichuan sauerkraut stored at 25, 35, and 45 °C were determined. Principal component analysis (PCA) and Fisher discriminant analysis (FDA) were used to analyze the e-tongue data. According to the above analysis, Sichuan sauerkraut with different storage times can be divided into three types: completely acceptable period, acceptable period, and unacceptable period. The model was found to be useful to determine the critical values of various quality indicators. Furthermore, the zero-order kinetic reaction model (R², 0.8699-0.9895) was fitted better than the first-order kinetic reaction model. The Arrhenius model (E_a value was 47.23-72.09 kJ/mol, k_ref value was 1.076 × 10⁶-9.220 × 10¹⁰ d^-1) exhibited a higher fitting degree than the Eyring model. Based on the analysis of physical properties, the shelf-life of Sichuan sauerkraut was more accurately predicted by the combination of the zero-order kinetic reaction model and the Arrhenius model, while the error back propagation artificial neural network (BP-ANN) model could better predict the chemical properties. It is a better choice for dealers and consumers to judge the shelf life and edibility of food by shelf-life model.

A Review on the Foodomics Based on Liquid Chromatography Mass Spectrometry

Due to the globalization of food production and distribution, the food chain has become increasingly complex, making it more difficult to evaluate unexpected food changes. Therefore, establishing sensitive, robust, and cost-effective analytical platforms to efficiently extract and analyze the food-chemicals in complex food matrices is essential, however, challenging. LC/MS-based metabolomics is the key to obtain a broad overview of human metabolism and understand novel food science. Various metabolomics approaches (e.g., targeted and/or untargeted) and sample preparation techniques in food analysis have their own advantages and limitations. Selecting an analytical platform that matches the characteristics of the analytes is important for food analysis. This review highlighted the recent trends and applications of metabolomics based on "foodomics" by LC-MS and provides the perspectives and insights into the methodology and various sample preparation techniques in food analysis.

Determination of exogenous prohibited flavour compounds added in coffee using gas chromatography triple quadrupole tandem massspectrometry and gas chromatography/combustion/isotope ratio mass spectrometry

An analytical method based on gas chromatography coupled to triple quadrupole tandem mass spectrometry (GC-MS/MS) was developed for the simultaneous determination of exogenous prohibited flavour compounds in coffee samples. In addition, gas chromatography/combustion/isotope ratio mass spectrometry (GC/C/IRMS) was developed to determine the origin of the founded prohibited flavour compound, N-methylpyrrole-2-carboxaldehyde (NMPCA). The good selectivity and sensitivity achieved in multiple reactions monitoring (MRM) mode allowed satisfactory confirmation and quantitation for the flavour compounds. The limits of detection (LODs) and limits of quantitation (LOQs) of these compounds were in the range of 0.0005-5.0 µg/kg and 0.002-16.0 µg/kg, respectively. The coffee samples were extracted with simultaneous distillation extraction (SDE) and NMPCA was analysed on a GC/C/IRMS system. The δ¹³C values of endogenous NMPCA in coffee beans were within a range of -35.0‰ to -31.1‰, whereas exogenous NMPCA was the range from -27.9‰ to -23.9‰. The validation results revealed that the GC-MS/MS method was sensitive and reliable, and the origin of NMPCA can be distinguished by GC/C/IRMS. Finally, this method was successfully applied to coffee samples analysis and NMPCA was found in coffee samples.

Unveiling the Molecular Basis of Mascarpone Cheese Aroma: VOCs analysis by SPME-GC/MS and PTR-ToF-MS

Mascarpone, a soft-spread cheese, is an unripened dairy product manufactured by the thermal-acidic coagulation of milk cream. Due to the mild flavor and creamy consistency, it is a base ingredient in industrial, culinary, and homemade preparations (e.g., it is a key constituent of a widely appreciated Italian dessert ‘Tiramisù’). Probably due to this relevance as an ingredient rather than as directly consumed foodstuff, mascarpone has not been often the subject of detailed studies. To the best of our knowledge, no investigation has been carried out on the volatile compounds contributing to the mascarpone cheese aroma profile. In this study, we analyzed the Volatile Organic Compounds (VOCs) in the headspace of different commercial mascarpone cheeses by two different techniques: Headspace-Solid Phase Microextraction-Gas Chromatography-Mass Spectrometry (HS-SPME GC-MS) and Proton-Transfer Reaction-Mass Spectrometry coupled to a Time of Flight mass analyzer (PTR-ToF-MS). We coupled these two approaches due to the complementarity of the analytical potential—efficient separation and identification of the analytes on the one side (HS-SPME GC-MS), and effective, fast quantitative analysis without any sample preparation on the other (PTR-ToF-MS). A total of 27 VOCs belonging to different chemical classes (9 ketones, 5 alcohols, 4 organic acids, 3 hydrocarbons, 2 furans, 1 ester, 1 lactone, 1 aldehyde, and 1 oxime) have been identified by HS-SPME GC-MS, while PTR-ToF-MS allowed a rapid snapshot of volatile diversity confirming the aptitude to rapid noninvasive quality control and the potential in commercial sample differentiation. Ketones (2-heptanone and 2-pentanone, in particular) are the most abundant compounds in mascarpone headspace, followed by 2-propanone, 2-nonanone, 2-butanone, 1-pentanol, 2-ethyl-1-hexanol, furfural and 2-furanmethanol. The study also provides preliminary information on the differentiation of the aroma of different brands and product types.

Multistage Signals Based on Cyclic Chemiluminescence for Decoding Complex Samples

Identification of complex samples presents a difficult challenge for modern analytical techniques, and the differentiation among closely similar mixtures often remains indeterminate. In this article, we designed a simplified cyclic chemiluminescence (CCL) system that is able to measure multistage signals in a single sample injection. The system was used to investigate the CCL reactions of the binary, ternary, and multicomponent mixtures. Results showed that each mixture has a unique exponential decay equation (EDE) with a constant decay coefficient (k-value) to describe the change law of its multistage signals. Further studies found that different brands of liquor, beer, toner, and baby powder have different k-values, and the same brand of the commodities between different batches have the same k-values, which allows facile identification of these complex samples. We then used different catalysts to design digital codes of the k-value for further improving the identifying ability of CCL. Moreover, the multistage signals are like fingerprints and could be used for linear discriminate analysis, which provides another complementary approach for identification of complex samples. Finally, we demonstrated that CCL shows potential applications in certification and quality assurance according to the change of the k-values of the sample. This work demonstrates that excellent discrimination ability of CCL for the identification of complex samples and provides a promising technology for quality assurance.