Comparative physiology and transcriptome response patterns in cold-tolerant and cold-sensitive varieties of Solanum melongena.

BACKGROUND: Climate change has led to severe cold events, adversely impacting global crop production. Eggplant (Solanum melongena L.), a significant economic crop, is highly susceptible to cold damage, affecting both yield and quality. Unraveling the molecular mechanisms governing cold resistance, including the identification of key genes and comprehensive transcriptional regulatory pathways, is crucial for developing new varieties with enhanced tolerance. RESULTS: In this study, we conducted a comparative analysis of leaf physiological indices and transcriptome sequencing results. The orthogonal partial least squares discriminant analysis (OPLS-DA) highlighted peroxidase (POD) activity and soluble protein as crucial physiological indicators for both varieties. RNA-seq data analysis revealed that a total of 7024 and 6209 differentially expressed genes (DEGs) were identified from variety "A" and variety "B", respectively. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment of DEGs demonstrated that the significant roles of starch and sucrose metabolism, glutathione metabolism, terpenoid synthesis, and energy metabolism (sucrose and starch metabolism) were the key pathways in eggplant. Weighted gene co-expression network analysis (WGCNA) shown that the enrichment of numerous cold-responsive genes, pathways, and soluble proteins in the MEgrep60 modules. Core hub genes identified in the co-expression network included POD, membrane transporter-related gene MDR1, abscisic acid-related genes, growth factor enrichment gene DELLA, core components of the biological clock PRR7, and five transcription factors. Among these, the core transcription factor MYB demonstrated co-expression with signal transduction, plant hormone, biosynthesis, and metabolism-related genes, suggesting a pivotal role in the cold response network. CONCLUSION: This study integrates physiological indicators and transcriptomics to unveil the molecular mechanisms responsible for the differences in cold tolerance between the eggplant cold-tolerant variety "A" and the cold-sensitive variety "B". These mechanisms include modulation of reactive oxygen species (ROS), elevation in osmotic carbohydrate and free proline content, and the expression of terpenoid synthesis genes. This comprehensive understanding contributes valuable insights into the molecular underpinnings of cold stress tolerance, ultimately aiding in the improvement of crop cold tolerance.

Exploring the Impact of Various Wooden Barrels on the Aromatic Profile of Aceto Balsamico Tradizionale di Modena by Means of Principal Component Analysis.

The study examines the unique production process of Aceto Balsamico Tradizionale di Modena PDO (ABTM), emphasizing its complex phases and the impact of raw materials and artisanal skill on its flavor characteristics. Analytical tests focused on the volatile composition of vinegars from different wood barrels at different aging stages, using solid-phase micro-extraction (SPME) coupled with gas chromatography, either with mass spectrometry (GC/MS) or flame ionization detector (FID). Multivariate analysis, including principal component analysis (PCA), was employed to investigate the presence of peculiarities among the volatile profiles of samples of different barrel origin. The research focuses on characterizing the volatile composition of vinegars sourced from individual wood barrels, such as Cherry, Chestnut, Mulberry, Juniper, and Oak. Although it was not possible to identify molecules directly connected to the woody essence, some similarities emerged between vinegar samples from mulberry and cherry barrels and between those of juniper and oak. The former group is characterized by analytes with high molecular weights, such as furfural and esters, while the latter group shows more intense peaks for ethyl benzoate. Moreover, ethyl benzoate appears to predominantly influence samples from chestnut barrels. Due to the highly complex production process of ABTM, where each battery is influenced by several factors, this study's findings are specific to the current experimental conditions.

Comparison of Semen Aesculi species from different geographical origins and horse chestnut based on metabolomics and chemometrics analysis.

BACKGROUND: Semen Aesculi, a traditional Chinese herbal medicine, has a long history of use for treating chest and abdominal pain with distension. In addition, the horse chestnut (Aesculus hippocastanum L.) is another species of Aesculus in Europe and has notable clinical significance in alleviating chronic venous insufficiency, hemorrhoids, and postoperative edema. Thus, highlighting the comparative study of Semen Aesculi and horse chestnut may broaden clinical applications. OBJECTIVES: To conduct a comprehensive comparative analysis on the chemical profiling of these two varieties and determine whether they have equivalent clinical efficacy by integrating plant metabolomics and multivariate statistical methods. METHODS: Initially, a comprehensive characterisation was performed using ultra-performance liquid chromatography quadrupole time-of-flight tandem mass spectrometry (UPLC-QTOF-MS/MS) platform, and in total 44 active ingredients were identified. Then, untargeted metabolomics combined with principal component analysis (PCA) and partial least squares-discriminant analysis (PLS-DA) was applied for the discrimination of a German species and three official Chinese species. Next, 24 marker compounds responsible for the discrimination of different species were screened out and used to predict the species of unknown samples by genetic algorithm-optimised support vector machine (GA-SVM) with a high prediction accuracy. Finally, a heatmap visualisation was employed for clarifying the distribution of the identified active ingredients. RESULTS: The three species of Chinese Semen Aesculi showed distinct separation from each other, while European horse chestnut and Aesculus chinensis Bunge were similar in chemical composition. CONCLUSIONS: This work provided experimental evidence for further expanding the clinical application of Chinese Semen Aesculi and promoted the species identification and quality control of Semen Aesculi.

Low Temperature Affects Fatty Acids Profiling and Key Synthesis Genes Expression Patterns in Zanthoxylum bungeanum Maxim.

Zanthoxylum bungeanum is one of the most important medicinal and edible homologous plants because of its potential health benefits and unique flavors. The chemical components in compositions and contents vary with plant genotype variations and various environmental stress conditions. Fatty acids participate in various important metabolic pathways in organisms to resist biotic and abiotic stresses. To determine the variations in metabolic profiling and genotypes, the fatty acid profiling and key differential genes under low temperature stress in two Z. bungeanum varieties, cold-tolerant (FG) and sensitive (FX), were investigated. Twelve main fatty acids were found in two Z. bungeanum varieties under cold stress. Results showed that the contents of total fatty acids and unsaturated fatty acids in FG were higher than those in FX, which made FG more resistant to low temperature. Based on the result of orthogonal partial least squares discriminant analysis, palmitic acid, isostearic acid, linolenic acid and eicosenoic acid were the important differential fatty acids in FG under cold stress, while isomyristic acid, palmitic acid, isostearic acid, stearic acid, oleic acid, linolenic acid and eicosenoic acid were the important differential fatty acids in FX. Furthermore, fatty acid synthesis pathway genes fatty acyl-ACP thioesterase A (FATA), Delta (8)-fatty-acid desaturase 2 (SLD2), protein ECERIFERUM 3 (CER3), fatty acid desaturase 3 (FAD3) and fatty acid desaturase 5 (FAD5) played key roles in FG, and SLD2, FAD5, 3-oxoacyl-[acyl-carrier-protein] synthase I (KAS I), fatty acyl-ACP thioesterase B (FATB) and acetyl-CoA carboxylase (ACC) were the key genes responding to low temperature in FX. The variation and strategies of fatty acids in two varieties of Z. bungeanum were revealed at the metabolic and molecular level. This work provides a reference for the study of chemical components in plant stress resistance.

Volatile Profiling of Magnolia champaca Accessions by Gas Chromatography Mass Spectrometry Coupled with Chemometrics.

Magnolia champaca (L.) Baill. ex Pierre of family Magnoliaceae, is a perennial tree with aromatic, ethnobotanical, and medicinal uses. The M. champaca leaf is reported to have a myriad of therapeutic activities, however, there are limited reports available on the chemical composition of the leaf essential oil of M. champaca. The present study explored the variation in the yield and chemical composition of leaf essential oil isolated from 52 accessions of M. champaca. Through hydrodistillation, essential oil yield was obtained, varied in the range of 0.06 ± 0.003% and 0.31 ± 0.015% (v/w) on a fresh weight basis. GC-MS analysis identified a total of 65 phytoconstituents accounting for 90.23 to 98.90% of the total oil. Sesquiterpene hydrocarbons (52.83 to 65.63%) constituted the major fraction followed by sesquiterpene alcohols (14.71 to 22.45%). The essential oils were found to be rich in ß-elemene (6.64 to 38.80%), γ-muurolene (4.63 to 22.50%), and ß-caryophyllene (1.10 to 20.74%). Chemometrics analyses such as PCA, PLS-DA, sPLS-DA, and cluster analyses such as hierarchical clustering, i.e., dendrogram and partitional clustering, i.e., K-means classified the essential oils of M. champaca populations into three different chemotypes: chemotype I (ß-elemene), chemotype II (γ-muurolene) and chemotype III (ß-caryophyllene). The chemical polymorphism analyzed in the studied populations would facilitate the selection of chemotypes with specific compounds. The chemotypes identified in the M. champaca populations could be developed as promising bio-resources for conservation and pharmaceutical application and further improvement of the taxa.

Terahertz spectroscopy technology as an innovative technique for food: Current state-of-the-Art research advances.

With the dramatic development of source and detector components, terahertz (THz) spectroscopy technology has recently shown a renaissance in various fields such as medical, material, biosensing and pharmaceutical industry. As a rapid and noninvasive technology, it has been extensively exploited to evaluate food quality and ensure food safety. In this review, the principles and processes of THz spectroscopy are first discussed. The current state-of-the-art applications of THz and imaging technologies focused on foodstuffs are then discussed. The advantages and challenges are also covered. This review offers detailed information for recent efforts dedicated to THz for monitoring the quality and safety of various food commodities and the feasibility of its widespread application. THz technology, as an emerging and unique method, is potentially applied for detecting food processing and maintaining quality and safety.

Comparative Assessment of Protein Kinase Inhibitors in Public Databases and in PKIDB.

Since the first approval of a protein kinase inhibitor (PKI) by the Food and Drug Administration (FDA) in 2001, 55 new PKIs have reached the market, and many inhibitors are currently being evaluated in clinical trials. This is a clear indication that protein kinases still represent major drug targets for the pharmaceutical industry. In a previous work, we have introduced PKIDB, a publicly available database, gathering PKIs that have already been approved (Phase 4), as well as those currently in clinical trials (Phases 0 to 3). This database is updated frequently, and an analysis of the new data is presented here. In addition, we compared the set of PKIs present in PKIDB with the PKIs in early preclinical studies found in ChEMBL, the largest publicly available chemical database. For each dataset, the distribution of physicochemical descriptors related to drug-likeness is presented. From these results, updated guidelines to prioritize compounds for targeting protein kinases are proposed. The results of a principal component analysis (PCA) show that the PKIDB dataset is fully encompassed within all PKIs found in the public database. This observation is reinforced by a principal moments of inertia (PMI) analysis of all molecules. Interestingly, we notice that PKIs in clinical trials tend to explore new 3D chemical space. While a great majority of PKIs is located on the area of "flatland", we find few compounds exploring the 3D structural space. Finally, a scaffold diversity analysis of the two datasets, based on frequency counts was performed. The results give insight into the chemical space of PKIs, and can guide researchers to reach out new unexplored areas. PKIDB is freely accessible from the following website: http://www.icoa.fr/pkidb.

A Comparative Characterization of Physicochemical and Antioxidants Properties of Processed Heterotrigona itama Honey from Different Origins and Classification by Chemometrics Analysis.

Stingless bee honey produced by Heterotrigona itama from different botanical origins was characterised and discriminated. Three types of stingless bee honey collected from acacia, gelam, and starfruit nectars were analyzed and compared with Apis mellifera honey. The results showed that stingless bee honey samples from the three different botanical origins were significantly different in terms of their moisture content, pH, free acidity, total soluble solids, colour characteristics, sugar content, amino acid content and antioxidant properties. Stingless bee honey was significantly different from Apis mellifera honey in terms of physicochemical and antioxidant properties. The amino acid content was further used in the chemometrics analysis to evaluate the role of amino acid in discriminating honey according to botanical origin. Partial least squares-discriminant analysis (PLS-DA) revealed that the stingless bee honey was completely distinguishable from Apis mellifera honey. Notably, a clear distinction between the stingless bee honey types was also observed. The specific amino acids involved in the distinction of honey were cysteine for acacia and gelam, phenylalanine and 3-hydroxyproline for starfruit, and proline for Apis mellifera honey. The results showed that all honey samples were successfully classified based on amino acid content.

Implementation of a Single Quadrupole Mass Spectrometer for Fingerprint Analysis: Venenum bufonis as a Case Study.

The mass spectrometry (MS) has been widely used for profiling chemical components of traditional Chinese medicine (TCM). However, there are few studies reporting quality control of TCM based on mass spectrometry fingerprint (MSF) due to its complicated operation and high cost. The aim of this study was to extend the application of MSF for quality evaluation of TCM. In this study, an MSF based on single quadrupole mass spectrometry method was established, and was successfully used for the quality control of Venenum bufonis (VB), a famous TCM which was used clinically for cancer treatment in China. The results showed that the superiority of MSF for more chemical information exposure and the finding of more potential chemical markers (eight versus four) compared with the traditional photo-diode array (a kind of ultra violet detector, PDA). Besides, the performance of MSF was also validated by similarity and principle component analysis (PCA) of MS data acquired on two other mass spectrometry (low-resolution, triple quadrupole, QQQ, and high-resolution, quadruple time-of-flight, Q-TOF), showing high consistency with QQQ and Q-TOF, but robustness with few parameters' settings. Based on our study, MSF could be widely applied for the quality control of TCM.

PKIDB: A Curated, Annotated and Updated Database of Protein Kinase Inhibitors in Clinical Trials.

The number of protein kinase inhibitors (PKIs) approved worldwide continues to grow steadily, with 39 drugs approved in the period between 2001 and January 2018. PKIs on the market have been the subject of many reviews, and structure-property relationships specific to this class of drugs have been inferred. However, the large number of PKIs under development is often overlooked. In this paper, we present PKIDB (Protein Kinase Inhibitor Database), a monthly-updated database gathering approved PKIs as well as PKIs currently in clinical trials. The database compiles currently 180 inhibitors ranging from phase 0 to 4 clinical trials along with annotations extracted from seven public resources. The distribution and property ranges of standard physicochemical properties are presented. They can be used as filters to better prioritize compound selection for future screening campaigns. Interestingly, more than one-third of the kinase inhibitors violate at least one Lipinski's rule. A Principal Component Analysis (PCA) reveals that Type-II inhibitors are mapped to a distinct chemical space as compared to orally administrated drugs as well as to other types of kinase inhibitors. Using a Principal Moment of Inertia (PMI) analysis, we show that PKIs under development tend to explore new shape territories as compared to approved PKIs. In order to facilitate the analysis of the protein space, the kinome tree has been annotated with all protein kinases being targeted by PKIs. Finally, we analyzed the pipeline of the pharmaceutical companies having PKIs on the market or still under development. We hope that this work will assist researchers in the kinase field in identifying and designing the next generation of kinase inhibitors for still untargeted kinases. The PKIDB database is freely accessible from a website at http://www.icoa.fr/pkidb and can be easily browsed through a user-friendly spreadsheet-like interface.

Metabolite Profiles, Bioactivity, and HPLC Fingerprint of Different Varieties of Eucommia ulmoides Oliv.: Towards the Utilization of Medicinal and Commercial Chinese Endemic Tree.

Eucommia ulmoides Oliv. is widely regarded in China as a precious medicinal and commercial endemic tree. Due to cross-breeding or natural variation of E. ulmoides, the metabolite composition may vary significantly, making control of the medical quality difficult. In order to improve the rational development and utilization, the quality of seven varieties of E. ulmoides were evaluated based on metabolite profiles (total phenolic, total flavonoid, gutta-percha, aucubin, geniposidic acid, chlorogenic acid, geniposide, pinoresinol diglucoside, rutin, hyperoside, and astragalin), bioactivities (in vitro, in vivo antioxidant activities, and antibacterial activities) and HPLC fingerprint combined with chemometrics analysis. On this basis, the differences of medicinal parts (leaf and bark) were further carried out. For the traditional use of bark, Purple-leaf E. ulmoides was the most suitable. For the use of leaf, Qinzhong 1 and Purple-leaf E. ulmoides were appropriate. HPLC fingerprint analysis showed that significant differences in metabolite profiles exist among seven varieties of E. ulmoides. Combined with chemometrics analysis, seven varieties of E. ulmoides were divided into three groups from the use of leaf and bark. The analysis not only evaluated quality of seven varieties of E. ulmoides, but also could distinguish different varieties and different regions of origin. The results can provide theoretical basis for E. ulmoides resources utilization and cultivation of fine varieties.

[Study on quality evaluation of Dihuang (Rehmannia glutinosa) by two-dimension HPLC fingerprints and chemometrics methods].

The study is to establish the two-dimension HPLC fingerprints of Dihuang (Rehmannia glutinosa), by HPLC-PDA and HPLC-ELSD methods. The separations were performed on Waters Atlantis®T3（4.6 mm× 250 mm，5 µm）and Welch Ultimate®Hilic-NH2（4.6 mm× 250 mm，5 µm）columns with the gradient elution of acetonitrile-0.01% phosphoric acid and acetonitrile-water, respectively. The chromatographic display wavelength for PDA detector was set at 203 nm. For HPLC-ELSD, the nebulizer was set as cooling mode, the drift tube temperature was set at 60 °C and the gas pressure was 35.0 psi. Based on similarity evaluation system for chromatographic fingerprint of traditional Chinese medicine, 26 and 10 chromatographic peaks were determined as common components for HPLC-PDA and HPLC-ELSD fingerprints, respectively. Chemometrics analyses, such as similarity analysis; cluster analysis and principal component analysis, were performed on the common peak areas in two-dimension fingerprints for 41 batches of Dihuang from multiple sources. The results showed that the HPLC-PDA fingerprint could distinguish dried rehmannia root between different sources, and HPLC-ELSD fingerprint could differentiate dried rehmannia root from prepared rehmannia root. The two-dimension fingerprints were established with advantages of a good degree of separation, abundant chemical information and multi-components identified including two nucleosides (adenosine and uridine)，four iridoid glycosides (catalpa alcohol，rehmaionoside D，rehmaionoside A and leonuride)，two phenylethanoid glycosides (acteoside and cistanoside A) and nine sugars. The method is simple and practical, which could be used for the identification and quality assessment for Dihuang.

Discrimination of Transgenic Maize Kernel Using NIR Hyperspectral Imaging and Multivariate Data Analysis.

There are possible environmental risks related to gene flow from genetically engineered organisms. It is important to find accurate, fast, and inexpensive methods to detect and monitor the presence of genetically modified (GM) organisms in crops and derived crop products. In the present study, GM maize kernels containing both cry1Ab/cry2Aj-G10evo proteins and their non-GM parents were examined by using hyperspectral imaging in the near-infrared (NIR) range (874.41-1733.91 nm) combined with chemometric data analysis. The hypercubes data were analyzed by applying principal component analysis (PCA) for exploratory purposes, and support vector machine (SVM) and partial least squares discriminant analysis (PLS-DA) to build the discriminant models to class the GM maize kernels from their contrast. The results indicate that clear differences between GM and non-GM maize kernels can be easily visualized with a nondestructive determination method developed in this study, and excellent classification could be achieved, with calculation and prediction accuracy of almost 100%. This study also demonstrates that SVM and PLS-DA models can obtain good performance with 54 wavelengths, selected by the competitive adaptive reweighted sampling method (CARS), making the classification processing for online application more rapid. Finally, GM maize kernels were visually identified on the prediction maps by predicting the features of each pixel on individual hyperspectral images. It was concluded that hyperspectral imaging together with chemometric data analysis is a promising technique to identify GM maize kernels, since it overcomes some disadvantages of the traditional analytical methods, such as complex and monotonous sampling.

Label-free Raman spectroscopy reveals tumor microenvironmental changes induced by intermittent fasting for the prevention of breast cancer in animal model.

The development of tools that can provide a holistic picture of the evolution of the tumor microenvironment in response to intermittent fasting on the prevention of breast cancer is highly desirable. Here, we show, for the first time, the use of label-free Raman spectroscopy to reveal biomolecular alterations induced by intermittent fasting in the tumor microenvironment of breast cancer using a dimethyl-benzanthracene induced rat model. To quantify biomolecular alterations in the tumor microenvironment, chemometric analysis of Raman spectra obtained from untreated and treated tumors was performed using multivariate curve resolution-alternative least squares and support vector machines. Raman measurements revealed remarkable and robust differences in lipid, protein, and glycogen content prior to morphological manifestations in a dynamically changing tumor microenvironment, consistent with the proteomic changes observed by quantitative mass spectrometry. Taken together with its non-invasive nature, this research provides prospective evidence for the clinical translation of Raman spectroscopy to identify biomolecular variations in the microenvironment induced by intermittent fasting for the prevention of breast cancer, providing new perspectives on the specific molecular effects in the tumorigenesis of breast cancer.

HPLC with quadrupole TOF-MS and chemometrics analysis for the characterization of Folium Turpiniae from different regions.

Folium Turpiniae has been used as a traditional Chinese medicine for the treatment of abscesses, fevers, gastric ulcers, and inflammations. This paper describes a strategy of combining HPLC with photodiode array detection and quadrupole TOF-MS, as well as phytochemical and chemometrics analysis for the characterization, isolation, and simultaneous quantification of the chemical constituents of Folium Turpiniae. 19 constituents were identified, namely, 11 flavonoids, seven gallic acid derivates, and quinic acid. Among them, 15 compounds were identified in this herbal medicine for the first time; compound 10 appears to be novel and was isolated and confirmed as ellagic acid-3-O-α-L-rhamnopyranoside by NMR spectroscopy and MS. In addition, nine marker compounds, namely gallic acid (2), ellagic acid-3-O-α-L-rhamnopyranoside (10), apigenin-7-O-(2''-rhamnosyl)gentiobioside (11), ellagic acid (12), luteolin-7-O-ß-D-neohesperidoside (13), ligustroflavone (14), 4'-O-methylellagic acid-3-O-α-L-rhamnopyranoside (16), rhoifolin (17), and neobudofficide (18), were quantified simultaneously in ten batches of Folium Turpiniae collected from different regions. Moreover, hierarchical clustering analysis and principal component analysis indicated that both samples from Hubei (S1) and Guangxi (S3) provinces showed apparent differences from the others. Samples from Jiangxi province (S2, S4, and S7-10) possessed similar properties and therefore belong to the same group.

Quantitative analysis of aflatoxin B1 of peanut by optimized support vector machine models based on near-infrared spectral features.

This study designs a chemometric framework for quantitatively evaluating aflatoxin B1 (AFB1) in peanuts based on near-infrared (NIR) spectroscopy technique. The NIR spectra of peanut samples exhibiting diverse fungal contamination levels were acquired using a portable NIR spectrometer. Subsequently, appropriate pre-processing techniques were employed for data refinement. To streamline the analysis, the iterative variable subset optimization (IVSO) technique was employed to conduct an initial screening of the pre-processed NIR spectra, eliminating numerous irrelevant variables. Building upon this screening process, the beluga whale optimization (BWO) algorithm was utilized to optimize the selected feature variables further. Subsequently, support vector machine (SVM) models were developed using the refined near-infrared spectral features to test AFB1 in peanuts quantitatively. The results indicate that the SVM model significantly improves detection performance and generalization proficiency, particularly after secondary optimization using BWO-IVSO. Among the different models considered, the SVM model established after BWO-IVSO optimization exhibited the most extraordinary level of generalization, with a root mean square error of prediction of 24.6322 µg∙kg-1, a correlation coefficient of 0.9761, and a relative percent deviation of 4.6999. Overall, this investigation highlights the effectiveness of the proposed NIR spectroscopy model based on BWO-IVSO-SVM for quantitatively analyzing AFB1 in peanuts. The study contributes valuable technical and methodological insights that can serve as a reference for rapidly determining mycotoxins in cereal crops.

Investigating the effect of different pre-treatment methods on Raman spectra recorded with different excitation wavelengths.

Raman reference libraries can be used for identification of components in unknown samples as Raman spectroscopy offers fingerprint information of the measured samples. Since Raman libraries often contain many different and/or highly similar spectra, it is important that the spectra are a reliable fingerprint for each compound. However, Raman spectra are highly sensitive to the experimental conditions, and the Raman spectra will change in different conditions even though the same sample is measured. Raman data pre-treatment minimizes the differences between Raman spectra arising from different experimental conditions. In this study, different combinations of pre-treatment methods are used to quantify the effect of each pre-treatment step in minimizing the differences between Raman spectra of the same sample in different experimental conditions, e.g., different excitation wavelengths. These different pre-treatment processes are evaluated for six solvents. The spectra differences between spectra recorded with three excitation wavelengths (532 nm, 633 nm, and 830 nm) are evaluated by angular difference index and the influence on a classification model is tested. The angular difference index of each spectrum after every data pre-treatment step shows a decreasing behavior. It could be demonstrated that wavenumber calibration has the largest effect on the differences between the Raman spectra. However, ω4 correction doesn't have a significate effect in this dataset. The classification results show that the prediction accuracy is improving by doing data pre-treatment. In the dataset obtained in 633 nm a lower amount of pre-treatment steps is needed but in the dataset 830 nm more pre-treatment steps are needed for a high accuracy. The result shows that the choice of an optimal pre-treatment method or combination of methods strongly influences the analysis results, but is far from straightforward, since it depends on the characteristics of the data set and the goal of data analysis.

Monitoring glucose levels in urine using FTIR spectroscopy combined with univariate and multivariate statistical methods.

The development of novel platforms for non-invasive continuous glucose monitoring applied in the screening and monitoring of diabetes is crucial to improve diabetes surveillance systems. Attenuated total reflection-Fourier transform infrared (ATR-FTIR) spectroscopy of urine can be an alternative as a sustainable, label-free, fast, non-invasive, and highly sensitive analysis to detect changes in urine promoted by diabetes and insulin treatment. In this study, we used ATR-FTIR to evaluate the urinary components of non-diabetic (ND), diabetic (D), and diabetic insulin-treated (D + I) rats. As expected, insulin treatment was capable to revert changes in glycemia, 24-h urine collection volume, urine creatinine, urea, and glucose excretion promoted by diabetes. Several differences in the urine spectra of ND, D, and D + I were observed, with urea, creatinine, and glucose analytes being related to these changes. Principal components analysis (PCA) scores plots allowed for the discrimination of ND and D + I from D with an accuracy of âˆ¼ 99 %. The PCA loadings associated with PC1 confirmed the importance of urea and glucose vibrational modes for this discrimination. Univariate analysis of second derivative spectra showed a high correlation (r: 0.865, p < 0.0001) between the height of 1074 cm-1 vibrational mode with urinary glucose concentration. In order to estimate the amount of glucose present in the infrared spectra from urine, multivariate curve resolution-alternating least square (MCR-ALS) was applied and a higher predicted concentration of glucose in the urine was observed with a correlation of 78.9 % compared to urinary glucose concentration assessed using enzyme assays. In summary, ATR-FTIR combined with univariate and multivariate chemometric analyses provides an innovative, non-invasive, and sustainable approach to diabetes surveillance.

Simultaneous Identification and Species Differentiation of Major Allergen Tropomyosin in Crustacean and Shellfish by Infrared Spectroscopic Chemometrics.

To solve the lack of rapid and accurate methods for allergen identification and traceability, an infrared spectroscopic chemometric analytical model (IR-CAM) was established by combining infrared spectroscopy with principal component and cluster analysis. By comparing the second derivative infrared (SD-IR) spectra of 5 proteins and 14 crustaceans and shellfish tropomyosin (TM), 8 shared peaks and unique fingerprint peaks in the amide III region were found for crabs, shrimps, and shellfish. Based on the unique fingerprint peaks coexisting with shared peaks, allergen TM in crustaceans and shellfish could be identified within 10 min (cf. ELISA âˆ¼ 4 h). Concurrently, the species differentiation of TM at the Class/Family level was achieved based on IR-CAM. Validation by fermented aquatic products TM (n = 60) demonstrated that the developed IR-CAM could simultaneously identify and differentiate TM in crustaceans and shellfish accurately. It could be applied for allergen detection and traceability of aquatic products on an antibody-free basis.

Metabolomic profiles of not from concentrate orange juice after different sterilization treatments based on HS-SPME-GC-MS and UPLC-QTOF-MS.

Not from concentrate (NFC) orange juice is minimally processed, natural-appearing food, and has become more popular. Sterilization is an important stage for NFC orange juice production. Here we present a comprehensive analysis of the effect of sterilization on the metabolites of NFC orange juices, including three thermal (pasteurization, high-temperature short time, and ultra-high temperature) and one nonthermal (high hydrostatic pressure) method. A total of 108 metabolites, including 59 volatiles and 49 nonvolatiles were identified in orange juice. Of which, only butyl butanoate and 3-carene were detected in fresh orange juice. Sterilization significantly changed the metabolites of orange juice, and different methods caused various changes. Esters were downregulated by both thermal and nonthermal sterilization, while most flavonoids and terpenes were upregulated. With comparative analyses of three thermal sterilization, we found that high temperature and relatively short-time treatment preserved esters and ascorbic acid more effectively than low temperature and prolonged treatment. Aldehydes, however, were the opposite. Nonthermal sterilization is effective in preserving the metabolites of orange juice, especially for esters, terpenes, and flavonoids. In addition, 19 distinct metabolites were characterized between thermal and nonthermal samples by chemometrics analysis. These findings provide a new sight of the optimization of sterilization methods and references for different types of NFC orange juice identification. PRACTICAL APPLICATION: This study provides a reference for the optimization of sterilization methods and identification of HHP and thermal NFC orange juice and also benefits the purchase of consumers.