An Innovative Metabolomic Approach for Golden Rum Classification Combining Ultrahigh-Performance Liquid Chromatography-Orbitrap Mass Spectrometry and Chemometric Strategies

Preliminary study on the geographical origin of Chinese 'Cuiguan' pears using integrated stable isotope and multi-element analyses

Distinguish the geographical origin of the pear is important due to the increasingly valued brand protection and reducing the potential food safety risks. In this study, the profiles of stable isotopes (δ13C, δ15N, δ2H, δ18O) and the contents of 16 elements in pear peer from four production areas were analyzed. The δ13C, δ15N, δ2H, δ18O and 12 elements were significantly different (p < 0.05) in the four production areas. Chemometrics analysis including principal component analysis (PCA), orthogonal partial least squares discriminant analysis (OPLS-DA) and linear discriminant analysis (LDA) were exploited for geographical origin classification of samples. OPLS-DA analysis showed that crucial variables (δ13C, δ18O, δ2H, Ni, Cd, Ca, δ15N, Sr and Ga) are more relevant for the discrimination of the samples. OPLS-DA achieved pear origin accuracy rates of 87.76 % by combining stable isotope ratios and elemental contents. LDA had a higher accuracy rate than OPLS-DA, and the LDA analysis showed that the original discrimination rate reached to 100 %, while the cross-validated rate reached to 95.7 %. These studies indicated that this method could be used to assess the geographical discrimination of pear from different producing areas and could potentially control the fair trade of pear in fruit markets.

In-situ analysis of trace components in proportioning distilled spirits using Raman integrating sphere spectroscopy

In situ and on-site analysis of trace components, such as methanol and ethyl acetate, in distilled spirits poses significant challenges. In this study, we have proposed a simple, yet effective and rapid approach that combines Raman spectroscopy with Raman integrating sphere technology to accurately detect trace constituents in distilled spirits. An external standard method to effectively separate overlapping Raman peaks from different substances are developed. Experimental results demonstrate that with an exposure time of 180 s under normal temperature and pressure, the detection limits for methanol, acetic acid, and ethyl acetate in proportioned distilled spirits are below 0.1 g/L. Importantly, the detection limit of methanol and acetic acid remains unaffected by the concentration of distilled spirits and the types of trace substances. Notably, the concentration of trace solute exhibits a highly linear relationship with its corresponding Raman intensity, offering a reliable probe for identifying unknown components in distilled spirits.

Volatile Compositions of Panax ginseng and Panax quinquifolium Grown for Different Cultivation Years

The present study examined the volatile profiles of Panax ginseng (Korean ginseng) and Panax quinquefolium (American ginseng) grown for different cultivation years by using HS-SPME/GC-MS and determined the key discriminant volatile compounds by chemometric analysis including principal component analysis (PCA), hierarchical cluster analysis (HCA), and partial least squares-discrimination analysis (PLS-DA). Fifty-six compounds, including forty terpenes, eight alcohols, one alkane, one ketone, and one furan, were identified in the ginseng roots. The chemometric results identified two major clusters of American ginseng and Korean ginseng cultivars with distinct volatile compositions. The volatile compounds in fresh white ginseng roots were affected by the species, but the influence of different cultivation ages was ambiguous. The major volatile components of ginseng roots are terpenes, including monoterpenes and sesquiterpenes. In particular, panaginsene, ginsinsene, α-isocomene, and caryophyllene were predominant in Korean ginseng cultivars, whereas β-farnesene levels were higher in American ginseng. The difference in volatile patterns between Panax ginseng and Panax quinquefolium could be attributed to the composition of sesquiterpenes such as β-panaginsene, ginsinsene, caryophyllene, and β-farnesene.

A novel multi-class classification model for schizophrenia, bipolar disorder and healthy controls using comprehensive transcriptomic data

Two common psychiatric disorders, schizophrenia (SCZ) and bipolar disorder (BP), confer lifelong disability and collectively affect 2% of the world population. Because the diagnosis of psychiatry is based only on symptoms, developing more effective methods for the diagnosis of psychiatric disorders is a major international public health priority. Furthermore, SCZ and BP overlap considerably in terms of symptoms and risk genes. Therefore, the clarity of the underlying etiology and pathology remains lacking for these two disorders. Although many studies have been conducted, a classification model with higher accuracy and consistency was found to still be necessary for accurate diagnoses of SCZ and BP. In this study, a comprehensive dataset was combined from five independent transcriptomic studies. This dataset comprised 120 patients with SCZ, 101 patients with BP, and 149 healthy subjects. The partial least squares discriminant analysis (PLS-DA) method was applied to identify the gene signature among multiple groups, and 341 differentially expressed genes (DEGs) were identified. Then, the disease relevance of these DEGs was systematically performed, including (α) the great disease relevance of the identified signature, (β) the hub genes of the protein-protein interaction network playing a key role in psychiatric disorders, and (γ) gene ontology terms and enriched pathways playing a key role in psychiatric disorders. Finally, a popular multi-class classifier, support vector machine (SVM), was applied to construct a novel multi-class classification model using the identified signature for SCZ and BP. Using the independent test sets, the classification capacity of this multi-class model was assessed, which showed this model had a strong classification ability.

Classification for psychiatric disorders including schizophrenia, bipolar disorder, and major depressive disorder using machine learning

Schizophrenia (SCZ), bipolar disorder (BP), and major depressive disorder (MDD) are the most common psychiatric disorders. Because there were lots of overlaps among these disorders from genetic epidemiology and molecular genetics, it is hard to realize the diagnoses of these psychiatric disorders. Currently, plenty of studies have been conducted for contributing to the diagnoses of these diseases. However, constructing a classification model with superior performance for differentiating SCZ, BP, and MDD samples is still a great challenge. In this study, the transcriptomic data was applied for discovering key genes and constructing a classification model. In this dataset, there were 268 samples including four groups (67 SCZ patients, 40 BP patients, 57 MDD patients, and 104 healthy controls), which were applied for constructing a classification model. First, 269 probes of differentially expressed genes (DEGs) among four sample groups were identified by the feature selection method. Second, these DEGs were validated by the literature review including disease relevance with the psychiatric disorders of these DEGs, the hub genes in the PPI (protein–protein interaction) network, and GO (gene ontology) terms and pathways. Third, a classification model was constructed using the identified DEGs by machine learning method to classify different groups. The ROC (receiver operator characteristic) curve and AUC (area under the curve) value were used to assess the classification capacity of the model. In summary, this classification model might provide clues for the diagnoses of these psychiatric disorders.

Multivariate Statistical Analysis of Metabolites in Anisodus tanguticus (Maxim.) Pascher to Determine Geographical Origins and Network Pharmacology

Anisodus tanguticus (Maxim.) Pascher, has been used for the treatment of septic shock, analgesia, motion sickness, and anesthesia in traditional Tibetan medicine for 2,000 years. However, the chemical metabolites and geographical traceability and their network pharmacology are still unknown. A total of 71 samples of A. tanguticus were analyzed by Ultra-Performance Liquid Chromatography Q-Exactive Mass Spectrometer in combination with chemometrics developed for the discrimination of A. tanguticus from different geographical origins. Then, network pharmacology analysis was used to integrate the information of the differential metabolite network to explore the mechanism of pharmacological activity. In this study, 29 metabolites were identified, including tropane alkaloids, hydroxycinnamic acid amides and coumarins. Principal component analysis (PCA) explained 49.5% of the total variance, and orthogonal partial least-squares discriminant analysis (OPLS-DA) showed good discrimination (R2Y = 0.921 and Q2 = 0.839) for A. tanguticus samples. Nine differential metabolites accountable for such variations were identified through variable importance in the projection (VIP). Through network pharmacology, 19 components and 20 pathways were constructed and predicted for the pharmacological activity of A. tanguticus. These results confirmed that this method is accurate and effective for the geographic classification of A. tanguticus, and the integrated strategy of metabolomics and network pharmacology can explain well the "multicomponent--multitarget" mechanism of A. tanguticus.

A flavoromics strategy for the differentiation of different types of Baijiu according to the non-volatile organic acids

Non-volatile organic acids (NVOAs) in 12 main flavor types of Baijiu were analyzed by a derivatization method combined with GC-MS and 38 NVOAs were quantified. Meanwhile, a flavoromics strategy based on the contents of NVOAs in the 12 flavor types of Baijiu was successfully used to the differentiation of Baijiu. PLS-DA models (explained variation, predictive capability) were used to consider different categories: fermentation process (0.931, 0.870), starter (0.921, 0.834), fermentation container (0.899, 0.810) and raw material (0.951, 0.909). Based on the selected categories, suitable separations were achieved, and the classification ability of these models were nearly 100%. As a result, the model demonstrated its ability to perfectly distinguish different types of Baijiu. Seventeen potential markers were identified by variable importance in projection method and were further processed using heatmap and hierarchical cluster analysis, indicating that the NVOAs had great discrimination power to differentiate Baijiu.

Comparison of Potent Odorants in Traditional and Modern Types of Chinese Xiaoqu Liquor (Baijiu) Based on Odor Activity Values and Multivariate Analyses

Predominant odorants in modern and traditional types of Chinese xiaoqu liquor (Baijiu) were identified and compared by the combined use of gas chromatography−olfactometry, odor activity values (OAVs), and multivariate analyses. A total of 79 aroma compounds were identified in a typical modern type xiaoqu Baijiu (M) and a typical traditional type xiaoqu Baijiu (T), 42 of them had OAV > 1 in both M and T samples. The main differences between the two samples were obtained for the concentration of 23 aroma-active compounds. A total of 22 samples made by different brewing processes were analyzed to confirm the differences. Partial least squares discriminant analysis confirmed that 20 compounds could be used as potential markers for discrimination between modern type xiaoqu Baijiu and traditional type xiaoqu Baijiu. Their difference in content is between 1.5 and 17.9 times for modern type xiaoqu Baijiu and traditional type xiaoqu Baijiu. The results showed the aroma characteristics of modern and traditional type xiaoqu Baijiu clearly and comprehensively, which will provide guidance for modern Baijiu quality control and evaluation.

Investigation of alterations in phospholipids during the production chain of infant formulas via HILIC-QTOF-MS and multivariate data analysis

Phospholipids play a key role in infant nutrition and cognitive function. In this study, hydrophilic interaction liquid chromatography coupled to quadrupole time-of-flight mass spectrometry method was firstly developed to analyze the composition of phospholipids. Then we characterized and quantified phospholipids extracted from raw, pasteurized, homogenized, and spray-dried milk to investigate the effect of the technological process on the composition of the phospholipids. Results indicate that the composition of the phospholipids underwent minor changes after pasteurization, while the concentration of phospholipids was significantly affected by the spray-drying process, especially phosphatidylethanolamine and phosphatidylinositol. Multivariate data analysis further verified the results and indicated that phospholipids containing polyunsaturated fatty acids had undergone significant changes during the production chain, especially in spray-drying. This work reveals the changes of phospholipids composition during the production chain of infant formulas and serve as a reference for the subsequent optimization of infant formulas to meet nutritional need of infants.

Chemical Analysis Combined with Multivariate Statistical Methods to Determine the Geographical Origin of Milk from Four Regions in China

Traceability of milk origin in China is conducive to the implementation of the protection of regional products. In order to distinguish milk from different geographical distances in China, we traced the milk of eight farms in four neighboring provinces of China (Inner Mongolia autonomous region, Hebei, Ningxia Hui autonomous and Shaanxi), and multivariate data analysis was applied to the data including elemental analysis, stable isotope analysis and fatty acid analysis. In addition, orthogonal partial least squares discriminant analysis (OPLS-DA) is used to determine the optimal classification model, and it is explored whether the combination of different technologies is better than a single technical analysis. It was confirmed that in the inter-provincial samples, the combination of the two techniques was better than the analysis using a single technique (fatty acids: R² = 0.716, Q² = 0.614; fatty acid-binding isotopes: R² = 0.760, Q² = 0.635). At the same time, milk produced by farms with different distances of less than 11 km in each province was discriminated, and the discriminant distance was successfully reduced to 0.7 km (Ningxia Hui Autonomous Region: the distance between the two farms was 0.7 km, R² = 0.771, Q² = 0.631). For short-distance samples, the combination multiple technologies are not completely superior to a single technique, and sometimes, it is easy to cause model over-fitting.

Molecular mechanism of lipid transformation in cold chain storage of Tan sheep

Cold chain (-20 °C) is one of the main transportation methods for storage of Tan sheep products. Lipids (66) in seven subclasses involved in sphingolipid, glycerophospholipid and fatty acid degradation metabolism were quantified in Tan sheep under cold chain storage, including fatty acyl carnitines, phosphatidylcholine (PC), lysophosphatidylcholine (LPC), phosphatidylethanolamine (PE), ceramides, sphingomyelin (SM) and lysophosphatidylethanolamine (LPE). Lipid transformation and molecular mechanism analyzed using fragmentation mechanisms and UHPLC-Q-Orbitrap MS/MS combined with lipidomics approaches determined transient increases of certain PC, PE and fatty acyl carnitine during the first 12 days of cold storage, subsequent declines of SM, PC, PE and fatty acyl carnitine, as well as increases of ceramide, LPC and LPE (24 days). These results offered insights into lipid transformation and quality of Tan sheep during cold chain storage.

Mass Spectrometry for Analysis of Changes during Food Storage and Processing

Many physicochemical changes occur during food storage and processing, such as rancidity, hydrolysis, oxidation, and aging, which may alter the taste, flavor, and texture of food products and pose risks to public health. Analysis of these changes has become of great interest to many researchers. Mass spectrometry is a promising technique for the study of food and nutrition domains as a result of its excellent ability in molecular profiling, food authentication, and marker detection. In this review, we summarized recent advances in mass spectrometry techniques and their applications in food storage and processing. Furthermore, current technical challenges associated with these methodologies were discussed.

Untargeted and targeted metabolomics strategy for the classification of strong aroma-type baijiu (liquor) according to geographical origin using comprehensive two-dimensional gas chromatography-time-of-flight mass spectrometry

A metabolomics strategy was developed to differentiate strong aroma-type baijiu (SAB) (distilled liquor) from the Sichuan basin (SCB) and Yangtze-Huaihe River Basin (YHRB) through liquid-liquid extraction coupled with GC×GC-TOFMS. PCA effectively separated the samples from these two regions. The PLS-DA training model was excellent, with explained variation and predictive capability values of 0.988 and 0.982, respectively. As a result, the model demonstrated its ability to perfectly differentiate all the unknown SAB samples. Twenty-nine potential markers were located by variable importance in projection values, and twenty-four of them were identified and quantitated. Discrimination ability is closely correlated to the characteristic flavor compounds, such as acid, esters, furans, alcohols, sulfides and pyrazine. Most of the marker compounds were less abundant in the SCB samples than in the YHRB samples. The quantitated markers were further processed using hierarchical cluster analysis for targeted analysis, indicating that the markers had great discrimination power to differentiate the SAB samples.