Intelligence detection of oil absorption in French fries by surface profiles.

Surface profiles are important evaluation indices for oil absorption behavior of fried foods. This research established two intelligent models of partial least-squares regression (PLSR) and back propagation artificial neural network (BP-ANN) for monitoring the oil absorption behavior of French fries based on the surface characteristics. Surface morphology and texture of French fries by rapeseed oil (RO) and high-oleic peanut oil (HOPO) at different temperatures were investigated. Results showed that oil content of samples increased with frying temperature, accounting for 37.7% and 41.4% of samples fried by RO and HOPO respectively. The increase of crust ratio, roughness and texture parameters (Fm, Nwr, fwr, Wc) and the decrease of uniformity were observed with the frying temperature. Coefficients of prediction set of PLSR and BP-ANN models were more than 0.93, which indicated that surface features combined with chemometrics were rapid and precise methods for determining the oil content of French fries.

Integration of untargeted and pseudotargeted metabolomics reveals specific markers for authentication and adulteration detection of Fritillariae Bulbus using tandem mass spectrometry and chemometrics.

Authentication and adulteration detection of closely related herbal medicines is a thorny issue in the quality control and market standardization of traditional Chinese medicine. Taking Fritillariae Bulbus (FB) as a case study, we herein proposed a three-step strategy that integrates mass spectrometry-based metabolomics and multivariate statistical analysis to identify specific markers, thereby accurately identifying FBs and determining the adulteration level. First, an ultra-high performance liquid chromatography-quadrupole time-of-flight mass spectrometry-based untargeted metabolomics method was employed to profile steroid alkaloids in five sorts of FB and screen potential differential markers. Then, the reliability of the screened markers was further verified by the distribution in different FB groups acquired from ultra-high performance liquid chromatography triple quadrupole mass spectrometry-based pseudotargeted metabolomics analysis. As a result, a total of 16 compounds were screened out to be the specific markers, which were successfully applied to distinguish five FBs by using discriminant analysis model. Besides, partial least squares regression models based on specific markers allowed accurate prediction of three sets of adulterated FBs. All the models afforded good linearity and good predictive ability with regression coefficient of prediction (R2p) > 0.9 and root mean square error of prediction (RMSEP) < 0.1. The reliable results of discriminant and quantitative analysis revealed that this proposed strategy could be potentially used to identify specific markers, which contributes to rapid chemical discrimination and adulteration detection of herbal medicines with close genetic relationship.

Quality evaluation of Lonicerae Japonicae Flos from different origins based on high-performance liquid chromatography (HPLC) fingerprinting and multicomponent quantitative analysis combined with chemical pattern recognition.

INTRODUCTION: Lonicerae Japonicae Flos (LJF) is widely used in food and traditional Chinese medicine. To meet demand, Lonicera japonica Thunb. is widely cultivated in many provinces of China. However, reported studies on the quality evaluation of LJF only used a single or a few active components as indicators, which could not fully reflect the quality of LJF. OBJECTIVES: In the present study, we aimed to develop a methodology for comprehensively evaluating the quality of LJF from different origins based on high-performance liquid chromatography (HPLC) fingerprinting and multicomponent quantitative analysis combined with chemical pattern recognition. MATERIALS AND METHODS: The HPLC method was developed for fingerprint analysis and was used to determine the contents of 19 components of LJF. To distinguish between samples and identify differential components, similarity analysis, hierarchical cluster analysis, principal component analysis, and orthogonal partial least squares discriminant analysis were performed. RESULTS: The HPLC fingerprint was established. Using the developed method, the contents of 19 components recognized in the fingerprint analysis were determined. Samples from different origins could be effectively distinguished. CONCLUSIONS: HPLC fingerprinting and multicomponent quantitative analysis combined with chemical pattern recognition is an efficient method for evaluating LJF.

Aroma and taste analysis of pickled tea from spontaneous and yeast-enhanced fermentation by mass spectrometry and sensory evaluation.

Anaerobically fermented pickled tea (PT) can be produced by spontaneous fermentation (SF) or yeast-enhanced fermentation (YF). Aroma and taste characteristics of PT during YF and SF were investigated using sensory evaluation, odour activity, aroma character impact values, HS-SPME-GC-MS, UPLC-QQQ-MS/MS, and spectrophotometry, annotating 198 volatile and 115 non-volatile components. The main contributing volatile components were ß-ionone, and 1-octanol, promoted by YF and SF, and yielding floral and fruity aromas respectively. Additionally, compared with SF, YF promoted the formation of citronellol yielding a floral aroma, inhibited the stale aroma of methoxybenzenes, and reduced bitter, astringent, and sour tastes. Furthermore, partial least-squares regression analysis identified the main components related to the 'acides aroma' of PT as linalool oxide, n-decanoic acid, hexanoic acid, 3,7-dimethyl-2,6-octadienoic acid, 3-methyl-1-dodecyn-3-ol, and nerolidol. This application could be used as methodology for the comprehensive analysis of tea aroma and taste and these results can act as guidelines for PT production and quality control.

Utilization of multiple-dilution fluorescence fingerprint facilitates prediction of chemical attributes in spice extracts.

Fluorescence Fingerprint (FF) is a powerful tool for rapid quality assessment of various foods and plant-derived products. However, the conventional utilization of FFs measured at a single dilution level (DL) to substitute chemical analyses is extremely challenging, especially for multicomponent materials like spice extracts because fluorescence intensity and concentration widely differ between components, with complex phenomena like inner filter effects. Here, we proposed a new strategy to use the meta-data comprised of FFs measured at multiple DLs with machine learning to estimate common chemical attributes including total polyphenol and flavonoid contents, and antioxidant abilities. This strategy achieved more consistently satisfactory performance in estimation of all chemical attributes of spice extracts compared to using a single DL. Hence, the workflow employed in this study is expected to serve as an alternative method to quickly evaluate the chemical quality of spice extracts, as well as other plant products and food materials.

[Manufacturing classification system for oral solid dosage forms of traditional Chinese medicines(â £): classification of hygroscopicity behaviors of capsules].

Hard capsules of traditional Chinese medicine(TCM) have different degrees of hygroscopicity, which affects the stability and efficacy of drugs. In this paper, 30 kinds of commercially available TCM capsules were used as the research object. The hygroscopicity curves of capsule contents, capsule shells, and capsules were tested respectively, and the first-order kinetic equation was used for fitting. The results show that during the 24 h hygroscopicity process, the capsule shell can reduce the weight gain caused by the hygroscopicity of the contents by 0.80%-53.0% and the hygroscopicity rate of the capsule contents by 1.74%-91.3%, indicating that the capsule shell has a strong delay effect on the hygroscopicity of the contents of the TCM capsules. Seven physical parameters of the contents of 30 kinds of TCM capsules were determined, and 14 prescription process-related parameters were sorted out. A partial least squares model for predicting the hygroscopicity rate of the contents of TCM capsules(with shell) for 24 h was established. It is found that the hygroscopicity rate of the capsule shell is positively correlated with the hygroscopicity of the contents of TCM capsules(with shell), suggesting that the capsule shell with a low hygroscopicity rate is helpful for moisture prevention. In addition, the pre-treatment process route of the preparation and the type of molding raw materials affect the hygroscopicity. A larger proportion of the extract in the capsule content and a smaller proportion of the fine powder of the decoction pieces indicate stronger hygroscopicity of the capsule content. The 24 h hygroscopicity rate of 15% was used as the classification node of hygroscopicity strength, and the hygroscopicity rate constant of 0.58 was used as the classification node of hygroscopicity speed. The classification system of hygroscopicity behaviors of TCM capsules was established: the varieties with strong and fast hygroscopicity accounted for about 6.67%, while those with strong and slow hygroscopicity accounted for about 33.3%; the varieties with weak and fast hygroscopicity accounted for about 26.7%, while those with weak and slow hygroscopicity accounted for about 33.3%. The classification system is helpful to quantify and compare the hygroscopicity behavior of different TCM capsules and provides a reference for the quality improvement, moisture prevention technologies, and material research of TCM capsules.

[Rapid detection technology of chemical component content in Lycii Fructus based on hyperspectral technology].

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Identification of key volatile and odor-active compounds in 10 main fragrance types of Fenghuang Dancong tea using HS-SPME/GC-MS combined with multivariate analysis.

Fenghuang Dancong tea (FHDC), a famous oolong tea originating from Guangdong Province in China, is known for its rich and unique fragrance. Nevertheless, the identification of the key aroma compounds with the difference fragrance types of FHDC remains uncertain. In order to characteristic the volatile components in different fragrance types of FHDC, 10 well-known fragrance types of FHDC and Tieguanyin (TGY) as a control were analyzed by headspace solid-phase microextraction (HS-SPME) coupled with gas chromatography mass spectrometry (GC-MS). Results indicated that 172 volatile compounds were identified as common volatile compounds among all the tea samples. A total of 16 compounds were identified as key compounds that could be used to distinguish between FHDC and TGY. Among the 10 FHDC fragrance types, indole, hotrienol, benzyl nitrile, and jasmine lactone were found to be the most abundant compounds. Despite the presence of certain similarities in aroma components, each type exhibits unique fragrance characteristics as a result of variation in compound composition content and proportion. Furthermore, using statistical and odor activity value analysis, 20 aroma-active compounds were recognized as potential characteristic markers accountable for the diverse fragrance types of FHDC. This research enhances our comprehension of the various fragrance types of FHDC and provides reference values for their rapid identification in the market.

Dose-effect relationship analysis of TCM based on deep Boltzmann machine and partial least squares.

A dose-effect relationship analysis of traditional Chinese Medicine (TCM) is crucial to the modernization of TCM. However, due to the complex and nonlinear nature of TCM data, such as multicollinearity, it can be challenging to conduct a dose-effect relationship analysis. Partial least squares can be applied to multicollinearity data, but its internally extracted principal components cannot adequately express the nonlinear characteristics of TCM data. To address this issue, this paper proposes an analytical model based on a deep Boltzmann machine (DBM) and partial least squares. The model uses the DBM to extract nonlinear features from the feature space, replaces the components in partial least squares, and performs a multiple linear regression. Ultimately, this model is suitable for analyzing the dose-effect relationship of TCM. The model was evaluated using experimental data from Ma Xing Shi Gan Decoction and datasets from the UCI Machine Learning Repository. The experimental results demonstrate that the prediction accuracy of the model based on the DBM and partial least squares method is on average 10% higher than that of existing methods.

Comparing Different Chemometric Approaches to Detect Adulteration of Cold-Pressed Flaxseed Oil with Refined Rapeseed Oil Using Differential Scanning Calorimetry.

Flaxseed oil is one of the best sources of n-3 fatty acids, thus its adulteration with refined oils can lead to a reduction in its nutritional value and overall quality. The purpose of this study was to compare different chemometric models to detect adulteration of flaxseed oil with refined rapeseed oil (RP) using differential scanning calorimetry (DSC). Based on the melting phase transition curve, parameters such as peak temperature (T), peak height (h), and percentage of area (P) were determined for pure and adulterated flaxseed oils with an RP concentration of 5, 10, 20, 30, and 50% (w/w). Significant linear correlations (p ≤ 0.05) between the RP concentration and all DSC parameters were observed, except for parameter h1 for the first peak. In order to assess the usefulness of the DSC technique for detecting adulterations, three chemometric approaches were compared: (1) classification models (linear discriminant analysis-LDA, adaptive regression splines-MARS, support vector machine-SVM, and artificial neural networks-ANNs); (2) regression models (multiple linear regression-MLR, MARS, SVM, ANNs, and PLS); and (3) a combined model of orthogonal partial least squares discriminant analysis (OPLS-DA). With the LDA model, the highest accuracy of 99.5% in classifying the samples, followed by ANN > SVM > MARS, was achieved. Among the regression models, the ANN model showed the highest correlation between observed and predicted values (R = 0.996), while other models showed goodness of fit as following MARS > SVM > MLR. Comparing OPLS-DA and PLS methods, higher values of R2X(cum) = 0.986 and Q2 = 0.973 were observed with the PLS model than OPLS-DA. This study demonstrates the usefulness of the DSC technique and importance of an appropriate chemometric model for predicting the adulteration of cold-pressed flaxseed oil with refined rapeseed oil.

Identifying the active ingredients of carbonized Typhae Pollen by spectrum-effect relationship combined with MBPLS, PLS, and SVM algorithms.

Typhae Pollen (TP) and its carbonized product (carbonized Typhae Pollen, CTP), as cut-and-dried herbal drugs, have been widely used in the form of slices in clinical settings. However, the two drugs exhibit a great difference in terms of their clinical efficacy, for TP boasts an effect of removing blood stasis and promoting blood circulation, while CTP typically presents a hemostatic function. Since the active ingredients of CTP, so far, still remain unclear, this study aimed at identifying the active ingredients of CTP by spectrum-effect relationship approach coupled with multi-block partial least squares (MBPLS), partial least squares (PLS), and support vector machine (SVM) algorithms. In this study, the chemical profiles of a series of CTP samples which were stir-fried for different duration (denoted as CTP0∼CTP9) were firstly characterized by UHPLC-QE-Orbitrap MS. Then the hemostatic effect of the CTP samples was evaluated from the perspective of multiple parameters-APTT, PT, TT, FIB, TXB2, 6-keto-PGF1α, PAI-1 and t-PA-using established rat models with functional uterine bleeding. Subsequently, MBPLS, PLS and SVM were combined to perform spectrum-effect relationship analysis to identify the active ingredients of CTP, followed by an in vitro hemostatic bioactivity test for verification. As a result, a total of 77 chemical ingredients were preliminarily identified from the CTP samples, and the variations occurred in these ingredients were also analyzed during the carbonizing process. The study revealed that all the CTP samples, to a varying degree, showed a hemostatic effect, among which CTP6 and CTP7 were superior to the others in terms of the hemostatic effect. The block importance in the projection (BIP) indexes of MBPLS model indicated that flavonoids and organic acids made more contributions to the hemostatic effect of CTP in comparison to other ingredients. Consequently, 9 bioactive ingredients, including quercetin-3-O-glucoside, kaempferol-3-O-rutinoside, quercetin, kaempferol, isorhamnetin, 2-methylenebutanedioic acid, pentanedioic acid, benzoic acid and 3-hydroxybenzoic acid, were further identified as the potential active ingredients based on PLS and SVM models as well as the in vitro verification. This study successfully revealed the bioactive ingredients of CTP associated with its hemostatic effect, and also provided a scientific basis for further understanding the mechanism of TP processing. In addition, it proposed a novel path to identify the active ingredients for Chinese herbal medicines.

Discrimination of Polygonati Rhizoma Species: An Investigation Utilizing High-Performance Liquid Chromatography Fingerprints and Chemometrics.

Polygonati Rhizoma has been a famous traditional Chinese medicine (TCM) for two thousand years. It is increasingly being used not just as a traditional herbal medicine but also as a popular functional food. In this study, qualitative and quantitative analysis of PR from three different origins were initially performed using chemical fingerprint and chemometrics methods. Hierarchical cluster analysis (HCA) and Principal component analysis (PCA) were used to classify 60 PR samples from three different origins. The results revealed that the PR samples fell into three clusters related to the origins. In addition, pairwise comparison of varying PR and obtaining chemical markers between different species through the establishment of partial least squares discriminant analysis. Finally, chemical markers 9,13 and 17 were identified by LC/MS as disporopsin, 5,7-dihydroxy-3-(4'-hydroxybenzyl)-6,8-dimethylchroman-4-one and (3R)-5,7-dihydroxy-3-(4'-hydroxybenzyl)-6-methylchroman-4-one or isomer, respectively. In conclusion, these methods can be applied to identify and distinguish the quality of PR with other original plants and provide novel ideas for evaluating herbal products used in TCM.

[Simultaneous determination of eleven volatile components in Cinnamomi Oleum by GC-MS].

A gas chromatography-triple quadrupole mass spectrometry(GC-MS) method was established for the simultaneous determination of eleven volatile components in Cinnamomi Oleum and the chemical pattern recognition was utilized to evaluate the quality of essential oil obtained from Cinnamomi Fructus medicinal materials in various habitats. The Cinnamomi Fructus medicinal materials were treated by water distillation, analyzed using GC-MS, and detected by selective ion monitoring(SIM), and the internal standards were used for quantification. The content results of Cinnamomi Oleum from various batches were analyzed by hierarchical clustering analysis(HCA), principal component analysis(PCA), and orthogonal partial least squares-discriminant analysis(OPLS-DA) for the statistic analysis. Eleven components showed good linear relationships within their respective concentration ranges(R~2>0.999 7), with average recoveries of 92.41%-102.1% and RSD of 1.2%-3.2%(n=6). The samples were classified into three categories by HCA and PCA, and 2-nonanone was screened as a marker of variability between batches in combination with OPLS-DA. This method is specific, sensitive, simple, and accurate, and the screened components can be utilized as a basis for the quality control of Cinnamomi Oleum.

Anti-inflammation activity of Zhizi Jinhua Pills and overall quality consistency evaluation based on integrated HPLC, DSC and electrochemistry fingerprints.

ETHNOPHARMACOLOGICAL RELEVANCE: Zhizi Jinhua Pills (ZZJHP), a compound preparation composed of 8 traditional Chinese medicines (TCM), is widely used clinically to clearing heat, purging fire, cooling blood and detoxifying. However, the studies on its pharmacological activity and the determination of active compounds are relatively few. There is a lack of quality control methods that can reflect the effectiveness of the drug. AIM OF THE STUDY: The objective was to construct fingerprint profiles, conduct a spectrum-effect relationship study and establish an overall quality control method for ZZJHP through anti-inflammatory and redox activity studies. MATERIALS AND METHODS: Firstly, anti-inflammatory activity was tested using the xylene-induced ear edema model in mice. Then, Five-wavelength fusion HPLC fingerprint, electrochemical fingerprint, and Differential scanning calorimetry (DSC) profiling were established to evaluate ZZJHP more comprehensively, where Euclidean quantified fingerprint method (EQFM) was proposed for the similarity assessment of these three fingerprints. Moreover, the spectrum-activity relationship of HPLC-FP and DSC-FP with electrochemical activity helped explore the active components or ranges in the fingerprint. Finally, integrated analysis of HPLC, DSC and electrochemistry were used for the quality screen of samples from different manufacturers. RESULTS: ZZJHP was found to significantly decrease the levels of both TNF-α and IL-6 in the mice. Qualitatively, the integrated similarity Sm of 21 samples were all greater than 0.9, indicating the great consistency in chemical composition. Quantitatively, 9 batches of samples were classified as Grade1∼4; 6 batches of samples were classified as Grade5∼7 due to higher PINT; 6 batches of samples were classified as Grade4∼5 due to lower PINT. EQFM can qualitatively and quantitatively characterize the fingerprint profile information from an overall perspective. CONCLUSIONS: This strategy will contribute to the quantitative characterization of TCM and promote the application of fingerprint technology in the phytopharmacy field.

The correlation between pharmacological activity and contents of eight constituents of Glycyrrhiza uralensis Fisch.

Licorice (Glycyrrhiza uralensis Fisch. (GUF), Leguminosae) has been extensively applied in traditional Chinese medicine (TCM) to treat diseases, exactly, in almost half of Chinese herbal prescription. However, the relationship between chemical contents and efficacy has not been established, which could evaluate GUF quality. To create a simple and effective quality-evaluation method, 33 batches of GUF from different habitats in China were collected. The correlation between eight constituents (liquiritin, isoliquiritin, liquiritigenin, isoliquiritigenin, glycyrrhizic acid, licochalcone A, glabridin and glycyrrhetinic acid) and pharmacological activities (anti-inflammatory, antioxidant and immunoregulatory) was analyzed per the partial least squares regression method. Results showed that eight constituents correlated significantly with the pharmacological activity. The correlation equation modes between pharmacological activity and contents of eight constituents were constructed and verified to be reliable. In GUF extract, the main constituents liquiritin, isoliquiritin and glycyrrhizic acid exhibited positive influence on anti-inflammatory and antioxidant effect with different potent, while the metabolites liquiritigenin and isoliquiritigenin exhibited positive effect on the immunoregulatory activity and glycyrrhetinic acid exhibited positive effect on all the tested activities. Thus, our chemical-efficacy correlation method is reliable and feasible to predict the pharmacological activity based on its eight constituents. It could be powerful in quality control of GUF and provides a useful way for quality evaluation of other medicinal herbs.

[Application of partial least squares algorithm to explore bioactive components of crude and stir-baked hawthorn for invigorating spleen and promoting digestion].

This study was aimed at identifying the bioactive components of the crude and stir-baked hawthorn for invigorating spleen and promoting digestion, respectively, to clarify the processing mechanism of hawthorn by applying the partial least squares(PLS) algorithm to build the spectrum-effect relationship model. Firstly, different polar fractions of crude and stir-baked hawthorn aqueous extracts and combinations of different fractions were prepared, respectively. Then, the contents of 24 chemical components were determined by ultra-high performance liquid chromatography-mass spectrometry. The effects of different polar fractions of crude hawthorn and stir-baked hawthorn aqueous extracts and combinations of different fractions were evaluated by measuring the gastric emptying rate and small intestinal propulsion rate. Finally, the PLS algorithm was used to establish the spectrum-effect relationship model. The results showed that there were significant differences in the contents of 24 chemical components for different polar fractions of crude and stir-baked hawthorn aqueous extracts and combinations of different fractions, and the gastric emptying rate and small intestinal propulsion rate of model rats were improved by administration of different polar fractions of crude and stir-baked hawthorn aqueous extracts and combinations of different fractions. The bioactive components of crude hawthorn identified by PLS models were vitexin-4″-O-glucoside, vitexin-2″-O-rhamnoside, neochlorogenic acid, rutin, gallic acid, vanillic acid, citric acid, malic acid, quinic acid and fumaric acid, while neochlorogenic acid, cryptochlorogenic acid, rutin, gallic acid, vanillic acid, citric acid, quinic acid and fumaric acid were the bioactive components of stir-baked hawthorn. This study provided data support and scientific basis for identifying the bioactive components of crude and stir-baked hawthorn, and clarifying the processing mechanism of hawthorn.

An Affordable NIR Spectroscopic System for Fraud Detection in Olive Oil.

Adulterations of olive oil are performed by adding seed oils to this high-quality product, which are cheaper than olive oils. Food safety controls have been established by the European Union to avoid these episodes. Most of these methodologies require expensive equipment, time-consuming procedures, and expert personnel to execute. Near-infrared spectroscopy (NIRS) technology has many applications in the food processing industry. It analyzes food safety and quality parameters along the food chain. Using principal component analysis (PCA), the differences and similarities between olive oil and seed oils (sesame, sunflower, and flax oil) have been evaluated. To quantify the percentage of adulterated seed oil in olive oils, partial least squares (PLS) have been employed. A total of 96 samples of olive oil adulterated with seed oils were prepared. These samples were used to build a spectra library covering various mixtures containing seed oils and olive oil contents. Eighteen chemometric models were developed by combining the first and second derivatives with Standard Normal Variable (SNV) for scatter correction to classify and quantify seed oil adulteration and percentage. The results obtained for all seed oils show excellent coefficients of determination for calibration higher than 0.80. Because the instrumental aspects are not generally sufficiently addressed in the articles, we include a specific section on some key aspects of developing a high-performance and cost-effective NIR spectroscopy solution for fraud detection in olive oil. First, spectroscopy architectures are introduced, especially the Texas Instruments Digital Light Processing (DLP) technology for spectroscopy that has been used in this work. These results demonstrate that the portable prototype can be used as an effective tool to detect food fraud in liquid samples.

Biosynthesis, behavior and fate of volatile organic sulfide in Lentinus edodes (Berk.) upon hot-air drying treatment.

This study elucidated the biosynthesis and changing behaviors of organic sulfide in shiitake mushrooms upon hot-air drying treatment. The changes of aw, moisture migration, contours of taste and flavor, organic sulfide, and 4 key enzyme activities were monitored throughout three drying procedures (CT/ST1/ST2). Results showed that drying rate was related to the moisture migration. Key enzymes of γ-GTase, ASFase and CS lyase were heat-resistant proteases, while C-Dase exhibited low thermal stability with the activity decreased during treatment. A total of 17 organic sulfides were identified and PLS analyses suggested 6 cyclic polysulfides were formed by C-Dase desulfurization, while 5 thioethers generation were related to the thermal cleavage of direct precursors (straight-chain di/tris/tetrasulfonyl esters) and Maillard reaction. These results indicated that ST2 drying procedures had a positive effect on the formation of cyclic polysulfides at the end of drying pried and the achievement of premium flavor qualities.

Predictive models built upon annotated and validated intake biomarkers in urine using paired or unpaired analysis helped to classify cranberry juice consumers in a randomized, double-blinded, placebo-controlled, and crossover study.

Intake biomarkers of cranberry juice in women can assess consumption in clinical trials. Discriminant biomarkers in urine may explain urinary tract infection (UTI) preventive activities. We hypothesized that validated and annotated discriminant metabolites in human urine could be used as intake biomarkers in building predictive multivariate models to classify cranberry consumers. Urine samples were collected from 16 healthy women aged 18 to 29 years at baseline and after 3- and 21-day consumption of cranberry or placebo juice in a double-blind, crossover study. Urine metabolomes were analyzed using ultra high-performance liquid chromatography coupled with Orbitrap mass spectrometry. Paired and unpaired multivariate analyses were used to annotate or identify discriminant metabolic features after cranberry consumption. Twenty-six discriminant metabolic features (paired analysis) and 27 (unpaired analysis) after cranberry consumption in an open-label intervention were rediscovered in the blinded study. These metabolites included exogenous (quinic acid) and endogenous ones (hippuric acid). The paired analysis showed better model fitting with partial least-square discriminant analysis models built on all metabolites than the unpaired analysis. Predictive models built on shared metabolites by the unpaired analysis were able to classify cranberry juice consumers with 84.4% to 100% correction rates, overall better than the paired analysis (50%-100%). The double-blind study validated discriminant metabolites from a previous open-label study. These urinary metabolites may be associated with the ability of cranberries to prevent UTIs and serve as potential cranberry intake biomarkers. It reveals the importance of selecting the right predictive models to classify cranberry consumers with higher than 95% correction rates.

Disease Incidence and Severity of Cercospora Leaf Spot in Sugar Beet Assessed by Multispectral Unmanned Aerial Images and Machine Learning.

Disease incidence (DI) and metrics of disease severity are relevant parameters for decision making in plant protection and plant breeding. To develop automated and sensor-based routines, a sugar beet variety trial was inoculated with Cercospora beticola and monitored with a multispectral camera system mounted to an unmanned aerial vehicle (UAV) over the vegetation period. A pipeline based on machine learning methods was established for image data analysis and extraction of disease-relevant parameters. Features based on the digital surface model, vegetation indices, shadow condition, and image resolution improved classification performance in comparison with using single multispectral channels in 12 and 6% of diseased and soil regions, respectively. With a postprocessing step, area-related parameters were computed after classification. Results of this pipeline also included extraction of DI and disease severity (DS) from UAV data. The calculated area under disease progress curve of DS was 2,810.4 to 7,058.8%.days for human visual scoring and 1,400.5 to 4,343.2%.days for UAV-based scoring. Moreover, a sharper differentiation of varieties compared with visual scoring was observed in area-related parameters such as area of complete foliage (AF), area of healthy foliage (AH), and mean area of lesion by unit of foliage ([Formula: see text]). These advantages provide the option to replace the laborious work of visual disease assessments in the field with a more precise, nondestructive assessment via multispectral data acquired by UAV flights.[Formula: see text] Copyright © 2023 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license.